LSTM-conformal forecasting-based bitcoin forecasting method for enhancing reliability.
Cryptocurrency is a new type of asset that has emerged with the advancement of financial technology, creating significant opportunities for research. bitcoin is the most valuable cryptocurrency and holds significant research value. However, due to the significant fluctuations in bitcoin's value...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2025-01-01
|
| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0319008 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849313900976144384 |
|---|---|
| author | Xiangyue Zhang Yuyun Kang Chao Li Wenjing Wang Keqing Wang |
| author_facet | Xiangyue Zhang Yuyun Kang Chao Li Wenjing Wang Keqing Wang |
| author_sort | Xiangyue Zhang |
| collection | DOAJ |
| description | Cryptocurrency is a new type of asset that has emerged with the advancement of financial technology, creating significant opportunities for research. bitcoin is the most valuable cryptocurrency and holds significant research value. However, due to the significant fluctuations in bitcoin's value in recent years, predicting its value and ensuring the reliability of these predictions, which have become crucial, have gained increasing importance. A method that combines Long Short-term Memory (LSTM) with conformal prediction is proposed in this paper. Initially, the high-dimensional features in the dataset are divided using the Spearman correlation coefficient method, and features below 0.75 and above 0.95 are excluded. Subsequently, an LSTM model is built, and data are fed into it and the data is used to train the model to generate predictions. Finally, the predicted values generated by the LSTM are fed into the conformal prediction model, and confidence intervals for these values are generated to verify their reliability. In the conformal prediction model, the quantile loss of the loss function is defined, and an Average Coverage Interval (ACI) predictor is designed to improve the accuracy of the results. The experiments are conducted using data from CoinGecko, which is a publicly available data. The results show that the LSTM-conformal prediction (LSTM-CP) combination improves reliability. |
| format | Article |
| id | doaj-art-4fe3e766903448969d884be8097e6f4e |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-4fe3e766903448969d884be8097e6f4e2025-08-20T03:52:38ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01205e031900810.1371/journal.pone.0319008LSTM-conformal forecasting-based bitcoin forecasting method for enhancing reliability.Xiangyue ZhangYuyun KangChao LiWenjing WangKeqing WangCryptocurrency is a new type of asset that has emerged with the advancement of financial technology, creating significant opportunities for research. bitcoin is the most valuable cryptocurrency and holds significant research value. However, due to the significant fluctuations in bitcoin's value in recent years, predicting its value and ensuring the reliability of these predictions, which have become crucial, have gained increasing importance. A method that combines Long Short-term Memory (LSTM) with conformal prediction is proposed in this paper. Initially, the high-dimensional features in the dataset are divided using the Spearman correlation coefficient method, and features below 0.75 and above 0.95 are excluded. Subsequently, an LSTM model is built, and data are fed into it and the data is used to train the model to generate predictions. Finally, the predicted values generated by the LSTM are fed into the conformal prediction model, and confidence intervals for these values are generated to verify their reliability. In the conformal prediction model, the quantile loss of the loss function is defined, and an Average Coverage Interval (ACI) predictor is designed to improve the accuracy of the results. The experiments are conducted using data from CoinGecko, which is a publicly available data. The results show that the LSTM-conformal prediction (LSTM-CP) combination improves reliability.https://doi.org/10.1371/journal.pone.0319008 |
| spellingShingle | Xiangyue Zhang Yuyun Kang Chao Li Wenjing Wang Keqing Wang LSTM-conformal forecasting-based bitcoin forecasting method for enhancing reliability. PLoS ONE |
| title | LSTM-conformal forecasting-based bitcoin forecasting method for enhancing reliability. |
| title_full | LSTM-conformal forecasting-based bitcoin forecasting method for enhancing reliability. |
| title_fullStr | LSTM-conformal forecasting-based bitcoin forecasting method for enhancing reliability. |
| title_full_unstemmed | LSTM-conformal forecasting-based bitcoin forecasting method for enhancing reliability. |
| title_short | LSTM-conformal forecasting-based bitcoin forecasting method for enhancing reliability. |
| title_sort | lstm conformal forecasting based bitcoin forecasting method for enhancing reliability |
| url | https://doi.org/10.1371/journal.pone.0319008 |
| work_keys_str_mv | AT xiangyuezhang lstmconformalforecastingbasedbitcoinforecastingmethodforenhancingreliability AT yuyunkang lstmconformalforecastingbasedbitcoinforecastingmethodforenhancingreliability AT chaoli lstmconformalforecastingbasedbitcoinforecastingmethodforenhancingreliability AT wenjingwang lstmconformalforecastingbasedbitcoinforecastingmethodforenhancingreliability AT keqingwang lstmconformalforecastingbasedbitcoinforecastingmethodforenhancingreliability |